Method for simulating and analyzing non-small cell lung cancer treatment action mechanism of acanthopanax based on network pharmacology, molecular docking and molecular dynamics
Through network pharmacology, molecular docking and molecular dynamics simulation methods, the mechanism of action of prickly Wu Plus on non-small cell lung cancer was analyzed, and the problem of difficult to reveal the potential targets and molecular mechanisms of prickly Wu Plus inhibiting lung cancer in the existing technology was solved, and the precise analysis of the mechanism of prickly Wu Plus treating lung cancer was achieved.
Patent Information
- Application Number
- CN202510333435.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-20
- Publication Date
- 2025-06-20
AI Technical Summary
The prior art is difficult to fully reveal the potential targets and molecular mechanisms of the inhibitory effect of stinger-five plus on non-small cell lung cancer.
Using methods based on network pharmacology, molecular docking and molecular dynamics simulation, the active ingredients of stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger stinger singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer singer sing
The mechanism of action of the stinger-five-added chemical components in the treatment of non-small cell lung cancer was accurately analyzed, the core targets and key active ingredients were determined, the drug-target interaction model was provided, the prediction results of molecular docking were verified, and the stability and dynamic characteristics of the drug-target were deeply explored.
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Figure CN120183746A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a research method for pharmacological mechanisms, and particularly to a method for analyzing the mechanism of action of Acanthopanax senticosus in the treatment of non-small cell lung cancer based on network pharmacology, molecular docking, and molecular dynamics simulation. Background Art
[0002] The conventional treatment methods for non-small cell lung cancer are surgical resection and chemotherapy, but the curative effect is not good, the prognosis is poor, and it is easy to relapse. The 5-year survival rate of patients with advanced NSCLC is only 15%, and the median survival period is about 1 year. Therefore, it is necessary to further explore the mechanism of the occurrence and development of NSCLC to find new targets for the treatment of NSCLC. Acanthopanax senticosus is a natural phytoalexin. Researchers have confirmed that Acanthopanax senticosus has inhibitory effects on various tumors, such as lung cancer, liver cancer, and gastric cancer. Acanthopanaxoside B can inhibit the growth and proliferation of lung cancer cells, induce apoptosis of lung cancer cells through the mitochondrial pathway, and induce autophagy of lung cancer cells. This research provides a research basis for the pharmacological action research between Acanthopanax senticosus and NSCLC, but the potential targets and molecular mechanisms of Acanthopanax senticosus in inhibiting lung cancer are still not fully understood.
[0003] Network pharmacology, molecular docking, and molecular dynamics simulation technologies play a synergistic role in the research of the treatment mechanism of non-small cell lung cancer. Network pharmacology reveals the molecular association rules between drugs and diseases by predicting the active ingredients of drugs and disease-related targets and constructing an "active ingredient-target-disease" network; molecular docking technology further simulates the binding modes and affinities of these active ingredients and targets, providing a structural basis for understanding the mechanism of drug action; while molecular dynamics simulation dynamically observes the changes of these complexes at the molecular level and evaluates their binding stability and dynamic characteristics. The combination of the three systematically analyzes the molecular mechanism of action of drugs in the treatment of non-small cell lung cancer, providing strong support for new drug research and development, drug optimization, and personalized treatment. Summary of the Invention
[0004] The purpose of the present invention is to provide a method for analyzing the mechanism of action of Acanthopanax senticosus in the treatment of non-small cell lung cancer based on network pharmacology, molecular docking, and molecular dynamics simulation, and accurately analyze the mechanism of action of the chemical components of Acanthopanax senticosus in the treatment of non-small cell lung cancer.
[0005] A method for analyzing the mechanism of action of Acanthopanax senticosus in the treatment of non-small cell lung cancer based on network pharmacology, molecular docking, and molecular dynamics simulation of the present invention is carried out in the following manner:
[0006] S1: Screening of active ingredients of Acanthopanax senticosus
[0007] According to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, screening conditions were set, and potential active ingredients of Acanthopanax senticosus were obtained and annotated according to the screening of OB and DL; the screening conditions were: OB > 30% and DL > 0.18;
[0008] S2: Obtain the potential targets of Acanthopanax senticosus chemical components in the treatment of non-small cell lung cancer and the disease targets of non-small cell lung cancer
[0009] The chemical components of Acanthopanax senticosus were collected in the SMILE In ChI and SDF structures using the Pub Chem database. Using the keyword "non-small cell lung cancer" in the Gene Cards database and screening with Score ≥ 5, a Venn diagram was drawn by Draw Venn Diagram to obtain the potential targets of Acanthopanax senticosus chemical components in the treatment of non-small cell lung cancer;
[0010] The expression profile data of the datasets "TCGA-NSCLC, TCGA-NSCLC" were obtained from the UCSC Xena database, and the relevant clinical information was obtained from GDC TCGA. Using the R limma package, differential analysis was performed on the TCGA data, and genes with significant differences were screened out as the disease targets of non-small cell lung cancer;
[0011] S3: Construction and analysis of the Acanthopanax senticosus chemical component-target-disease network
[0012] Using Cytoscape_, the relationship network among the active ingredients obtained in S1, the potential targets of Acanthopanax senticosus chemical components in the treatment of non-small cell lung cancer obtained in S2, and the disease targets of non-small cell lung cancer was constructed;
[0013] S4: Construction of the protein-protein interaction network
[0014] The STRING database was used to analyze the common targets of Acanthopanax senticosus chemical components and non-small cell lung cancer in the network constructed in S3, and the Cytoscape software was used to draw the protein-protein interaction network diagram, establish the protein-protein interaction network, and obtain the protein-protein interaction network relationship table;
[0015] S5: Explore the specific mechanism of anti-non-small cell lung cancer by KEGG pathway enrichment analysis and GO function enrichment analysis
[0016] The Metascape database was used to perform KEGG pathway enrichment analysis and GO function enrichment analysis on the potential targets of Acanthopanax senticosus chemical components in the treatment of non-small cell lung cancer to obtain GOCC, GOMF, GOBP, and KEGG-related data;
[0017] S6: Construction and Analysis of the Chemical Constituents-Targets-Pathways-Diseases Network of Acanthopanax senticosus
[0018] The "Chemical Constituents-Targets-Pathways-Diseases" network of Acanthopanax senticosus was constructed by selecting the TOP pathways, the key targets therein, and the active ingredients screened in S1;
[0019] S7: Molecular Docking
[0020] Molecular docking was performed on the two key targets with the highest degree values in the S4 protein-protein interaction network diagram and the main active ingredients, and it was found that the interaction between the core targets and the key components of Acanthopanax senticosus is closely related to lung cancer; among them, the key components of Acanthopanax senticosus are emodin, quercetin, and kaempferol, and the core targets are CCL2, IL6, and MMP9; the 3D structures of the main active chemical constituents of Acanthopanax senticosus were downloaded from the PubChem database, and the 3D structures of the core target proteins were downloaded from the Protein Data Bank database;
[0021] S8: Molecular Dynamics Simulation to Evaluate the Binding Stability of the Protein-Ligand Complex
[0022] Furthermore, the key components of Acanthopanax senticosus described in S7 are emodin, quercetin, and kaempferol, and the core targets are CCL2, IL6, and MMP9..
[0023] Furthermore, after collecting the chemical constituents of Acanthopanax senticosus described in S2 in the SMILE In ChI and SDF structures using the PubChem database, they were imported into Swiss ADME for evaluation of related indicators, and imported into Target Net / BATMAN-TCM / SwissTarget Prediction for target prediction. The obtained targets were summarized and de-duplicated using Uniprot specifications.
[0024] Furthermore, after obtaining the relevant clinical information from GDC TCGA described in S2, the sample data with incomplete clinical information was excluded, and a total of 375 normal and 1011 non-small cell lung cancer transcriptome data were obtained.
[0025] Furthermore, for the disease targets of non-small cell lung cancer screened in S2, the disease targets of non-small cell lung cancer were screened for differentially expressed genes according to |logFC|>1.5 and P<0.05. The heatmap was drawn using the pheatmap 1.0.12 package in R language, and the volcano plot was drawn using the ggplot 23.4.2 package.
[0026] The drawing of gene screening, volcano plots, and heatmaps aims to verify the gene expression characteristics of disease-related target genes in non-small cell lung cancer through differential expression analysis, ensuring that the target library for subsequent network pharmacology analysis has biological relevance. This step screens out key targets through statistical significance (volcano plots) and expression pattern clustering (heatmaps), directly affecting the target priority selection for molecular docking and kinetic simulations, and is the basis for constructing a multi-level mechanism model of 'ingredient-target-pathway'.
[0027] Furthermore, the protein-protein interaction network is established in S4 as follows:
[0028] The common targets of Acanthopanax senticosus chemical components and non-small cell lung cancer obtained from the network diagram constructed in S3 are imported into the STRING database to establish a PPI network. The species is selected as Homo sapiens, the confidence level is set to ≥0.4, free nodes are hidden, the TSV file is found and downloaded, and the PPI network diagram is drawn using cytospace to establish the PPI network diagram.
[0029] Furthermore, in S5, for GO enrichment analysis, with P < 0.05 as the screening condition, the top 20 biological terms with P values less than 0.05 in the results are selected to draw a bar chart; for KEGG enrichment analysis, with P < 0.05 as the significant enrichment screening condition, the top 30 signaling pathways with P < 0.05 are selected to draw a column chart and a bubble chart.
[0030] Furthermore, in S7, the 3D structure of the core target protein is downloaded from the Protein Data Bank database. The core target protein is preliminarily processed using Pymol 2.6.0 software, and then the protein and ligand molecules are pre-processed using AutoDock Tools1.5.7 software.
[0031] Furthermore, in S7, the molecular docking of key targets and main active ingredients is completed by Autodock Vina. After the molecular docking is completed, the docking results are visualized using Py MOL software.
[0032] Furthermore, the binding stability of the protein-ligand complex is evaluated in S8 molecular dynamics simulation as follows: The complex obtained from molecular docking is subjected to 100 ns molecular dynamics simulation using Gromacs v2023.4 software. The protein topology structure is constructed using the pdb2gmx module, and the force field is Amber14sb; the complex format is converted, the force field is added, solvated and the charge is adjusted. After energy minimization, the system is heated and equilibrated, and a 100 ns simulation is performed. Various parameters are calculated, the free energy is analyzed using Gromacs, and the binding free energy is extracted and calculated and visualized.
[0033] The present invention includes the following beneficial effects:
[0034] (1) The present invention innovatively improves the conventional methods of network pharmacology. By directly using various databases for efficient screening, there is no need to master complex computer languages such as R language, greatly simplifying the learning threshold of network pharmacology. At the same time, the data mining method adopted by the present invention can deeply explore the functions of various databases. Combining network pharmacology, molecular docking and molecular dynamics techniques makes the research results more accurate, and comprehensively and detailedly analyzes the mechanism of action of Acanthopanax senticosus on non-small cell lung cancer.
[0035] (2) Network pharmacology technology helps the present invention determine the core protein targets of Acanthopanax senticosus that play a key role in non-small cell lung cancer. Molecular docking technology predicts the binding sites of the active components of Acanthopanax senticosus with these target proteins, providing a preliminary drug-target interaction model. Further, molecular dynamics research deeply explores the stability and dynamic characteristics of the drug-target by simulating the dynamic changes of the complex in the physiological environment, verifies the prediction results of molecular docking, and provides more detailed interaction information. The combination of these technologies enables us to more accurately reveal the molecular mechanism of Acanthopanax senticosus in the treatment of non-small cell lung cancer and accelerates the prediction process of drug efficacy.
[0036] (3) The present invention's research shows that the key components of Acanthopanax senticosus in inhibiting non-small cell lung cancer are emodin, quercetin, and kaempferol, and the core targets are CCL2, IL6, and MMP9, providing a new exploration method for researchers. Through this method, researchers can quickly reveal the potential mechanism of the chemical components of Acanthopanax senticosus in the treatment of non-small cell lung cancer based on the interaction between the therapeutic chemical components of Acanthopanax senticosus and target proteins, greatly simplifying the drug development process. Description of the Drawings
[0037] Figure 1 It is a Venn diagram of Acanthopanax senticosus targets and NSCLC targets;
[0038] Figure 2 It is a heat map of differential expression analysis and a volcano plot of differential expression analysis;
[0039] Figure 3 It is a network diagram of Acanthopanax senticosus chemical components - potential targets - diseases;
[0040] Figure 4 It is a network diagram of the interaction of target proteins of Acanthopanax senticosus in the treatment of NSCLC;
[0041] Figure 5 It is a bar chart of GO enrichment analysis of targets of Acanthopanax senticosus in the treatment of NSCLC;
[0042] Figure 6 It is a bubble chart of KEGG pathway enrichment analysis of targets of Acanthopanax senticosus in the treatment of NSCLC;
[0043] Figure 7 The main pathway-target network of Acanthopanax senticosus in anti-lung cancer;
[0044] Figure 8 It is the molecular docking diagram of the main active ingredients and key target molecules. Figure A: Schematic diagram of molecular docking of CCL2 and emodin. Figure B: Schematic diagram of molecular docking of CCL2 and quercetin. Figure C: Schematic diagram of molecular docking of CCL2 and kaempferol. Figure D: Schematic diagram of molecular docking of IL6 and emodin. Figure E: Schematic diagram of molecular docking of IL6 and quercetin. Figure F: Schematic diagram of molecular docking of IL6 and kaempferol. Figure G: Schematic diagram of molecular docking of CCL2 and emodin. Figure H: Schematic diagram of molecular docking of CCL2 and quercetin. Figure I: Schematic diagram of molecular docking of CCL2 and kaempferol;
[0045] Figure 9 It is the molecular dynamics simulation analysis of the top two in molecular docking scores. A: RMSD change curve; B: Amino acid residue fluctuation; C: Number of H bonds; D: RG curve; E: SASA fluctuation curve; F: Free energy distribution map; G: Protein-ligand binding free energy; H: Protein amino acid residue energy decomposition. Specific implementation manners
[0046] To make the purposes, technical solutions and advantages of the embodiments of the present invention clearer and more understandable, the spirit of the content disclosed by the present invention will be described in detail below. Any person skilled in the art in the technical field, after understanding the embodiments of the content of the present invention, can make changes and modifications according to the technologies taught by the content of the present invention, and it does not deviate from the spirit and scope of the content of the present invention.
[0047] The schematic embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.
[0048] 1 Screening of active ingredients
[0049] In the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http: / / tcmspw.com / tcmsp.php), according to the literature reports and screening in the SwissADME database (http: / / www.swissadme.ch / index.php), map to the pubchem to retrieve the effective structural information of the chemical components of Acanthopanax senticosus, and a total of 122 kinds of effective structural information of components are obtained. Import SwissADME to evaluate the druglikeness and other indicators of the chemical components of Acanthopanax senticosus, with OB≥30% and DL≥0.18, and a total of 57 active ingredients are screened. Target prediction is carried out by TargetNet / BATMAN-TCM / Swiss Target Prediction. After deleting duplicate targets, a total of 939 chemical component targets of Acanthopanax senticosus are obtained. The results are shown in Figure 1 .
[0050] 2. Obtain disease targets
[0051] The expression profile data of the datasets "TCGANSCLC, TCGA-NSCLC" were obtained from the UCSC Xena database, and the relevant clinical information was obtained from GDC TCGA. After excluding the sample data with incomplete clinical information, a total of 375 normal and 1011 tumor non-small cell lung cancer transcriptome data were obtained. Using the R limma 3.54.2 package, differential analysis was performed on the TCGA data, and the genes with significant differences were screened out as the disease targets. The differential expression genes were screened according to |logFC| > 1.5 and P < 0.05. A total of 3943 differential expression genes were obtained, including 1991 down-regulated genes and 1952 up-regulated genes; the potential targets of the disease were selected and intersected with the 939 targets of the active ingredients of Acanthopanax senticosus. After mapping the two, 140 common action targets were determined as the potential targets of the Acanthopanax senticosus chemical components acting on the disease. The pheatmap 1.0.12 package in R language was used to draw the heat map, and the ggplot 23.4.2 package was used to draw the volcano plot. See Figure 2 .A (where red represents up-regulated gene expression and blue represents down-regulated gene expression) and Figure 2 .B
[0052] 3. Construction and analysis of the Acanthopanax senticosus chemical component-target-disease network
[0053] The one-to-one correspondence between the active ingredients of Acanthopanax senticosus and the targets was imported into the Cytoscape 3.9.1 software to construct the Acanthopanax senticosus chemical component-target-disease network diagram. The nodes represent the Acanthopanax senticosus chemical components and the targets respectively, and the edges represent the interaction relationship between the Acanthopanax senticosus chemical components and the targets. The Acanthopanax senticosus chemical component-target-disease network diagram has 199 nodes and 1013 edges, and the edges represent the targeting relationship between the Acanthopanax senticosus chemical components and the targets. Among them, 57 active ingredients act on 140 targets (some of the Acanthopanax senticosus chemical component targets are filtered out because they are not in the intersection). The NetworkAnalyzer plugin was used to analyze the network topology properties. In the network diagram, the size of the node represents the degree value of the node. The larger the degree value, the stronger the hub role of the node in the network. The results are shown in Figure 3 .
[0054] 4. Construction and analysis of the protein-protein interaction (PPI) network
[0055] The protein-protein interaction (PPI) network diagram was constructed using the STRING database. The intersection targets related to the treatment of diseases with the chemical components of Acanthopanax senticosus were imported into the STRING database. The species was selected as "Homo sapiens" and the confidence was set to ≥0.4. The interaction relationship between proteins was obtained and saved as TSV and PNG files. The files were imported into the Cytoscape software, and isolated targets without intersection were deleted. The network topology properties were analyzed, and the core targets were screened according to the degree ranking of the topological structure (CC / BC / degree ranking). The core targets were screened using the cytohubba plug-in MCC algorithm. The results are shown in Figure 4 .
[0056] Table 1 Some targets in the PPI network of Acanthopanax senticosus and non-small cell lung cancer
[0057]
[0058] 5.GO and KEGG enrichment analysis
[0059] The 140 chemical components of Acanthopanax senticosus and disease-related targets were analyzed by gene ontology (GO) functional enrichment analysis and pathway enrichment analysis based on Kyoto encyclopedia of genes and genomes (KEGG) using R clusterProfiler4.6.2 package. The top 15 entries of biological process (BP), molecular function (MF), and cellular component (CC) with P < 0.05 were selected to draw bar graphs. KEGG pathway enrichment analysis was performed on the targets, and a total of 69 signal pathways were enriched. This study selected the top 30 pathways with P < 0.05 in the downloaded results and drew bubble graphs for visualization analysis. Results are shown in Figure 5 and Figure 6 .
[0060] 6. Construction and analysis of the chemical components-target-pathway-disease network of Acanthopanax senticosus
[0061] The core TOP pathways and their key targets and active ingredients were selected to construct the "Acanthopanax chemical components-targets-pathways-diseases" network. The network consists of 229 nodes and 1,125 edges (140 targets, 30 signaling pathways and diseases, Acanthopanax chemical components). Different node shapes represent different active ingredients, key targets and regulatory pathways in Acanthopanax, and the color depth represents the degree weight in the network. The results are shown inFigure 7 。
[0062] 7. Molecular docking
[0063] Select the top two key targets with the highest degree values in the PPI interaction network for molecular docking. The 3D structures of the main active chemical components in Acanthopanax senticosus are downloaded from the PubChem database, and the 3D structures of the core target proteins are downloaded from the Protein Data Bank (https: / / www.rcsb.org / ) database. Use Pymol 2.6.0 software to perform preliminary processing on the core target proteins, such as removing solvent molecules, and then use AutoDock Tools 1.5.7 for further hydrogenation and charge addition processing. Save the core target proteins and active Acanthopanax senticosus chemical components as files in the "pdbqt" format, and set appropriate grid positions and sizes. Finally, AutodockVina is used to complete the docking of the components and targets. The docking results are visualized using PyMOL software. According to the main active components of the Acanthopanax senticosus anti-non-small cell lung cancer interaction network, select 3 components with the most targets in the gene network and the top 10 core targets in the PPI network for molecular docking. The docking energy results are shown in Table 2. The smaller the binding energy, the higher the binding activity, and the easier it is for the Acanthopanax senticosus chemical components to bind to the target. A binding energy ≤ -5.0 kcal / mol indicates good binding, and a binding energy ≤ -7.5 kcal / mol indicates very good binding. The molecular docking results are shown in Figure 8 。
[0064] Table 2 Binding energies of ligands and receptors
[0065]
[0066] 8. Molecular dynamics simulation
[0067] The complex obtained by molecular docking was subjected to 100 ns molecular dynamics simulations using Gromacs v2023.4 software. The pdb2gmx module was used to construct the protein topology, and the Amber14sb force field was adopted. (1) The pdb format of the complex was converted to the gro format, and the complex was regarded as the initial structure for MD simulations (Molecular dynamic simulation); (2) The GAFF2 (Generalized Amber Force Field) force field was added to the small molecule using AmberTools22 software; (3) The three-point transferable intermolecular potential (TIP3P) solvent was selected to dissolve the complex, and the minimum distance between the protein atoms and the edge of the water box was at least 1.2 nm. The Particle Mesh Ewald (PME) method was used to calculate the long-range electrostatic interactions and the charges of the simulation system were neutralized by adding an appropriate number of Na+ and Cl-; (4) To obtain a stable system, the steepest descent algorithm was used for energy minimization (EM); (5) The solute was restrained in the isothermal-isochoric (NVT) system, and the system was slowly heated from 0 K to 300 K, and then equilibrated at a temperature of 300 K and a pressure of 1 Bar (1 Bar = 1×105 Pa) in the isothermal-isobaric (NPT) ensemble; (6) A 100 ns molecular dynamics simulation of the complex was performed and the simulation trajectory was saved for subsequent analysis. According to the results of the MD simulations, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg) value, solvent accessible surface area (SASA), and hydrogen bonds (H-bonds) of the complex were calculated. Based on the RMSD and Rg values, the Gibbs free energy was calculated using the built-in g_sham and xpm2txt modules of Gromacs software, and the free energy distribution map was plotted. The free energy distribution map was used to describe the conformation with the minimum energy during the entire process of the structural dynamics simulation of the complex. If the interaction between the protein and the ligand is very weak or unstable, multiple minimum energy clusters will appear in the free energy distribution map; strong and stable interactions can form an almost single energy cluster in the energy distribution, and DuIvyTools 0.6.0 was used for plotting. In addition, the last 10 ns data after the system reached equilibrium was extracted to calculate the binding free energy, and gmx_MMPBSA was used to analyze the binding free energy of the complex by molecular mechanics / Poisson-Boltzmann surface area (MMPBSA). MMPBSA_ana was used for the visualization of the binding energy analysis results. The results are shown in Figure 9 (In the figure, VDWAALS, EEL, EPB, ENPOLAR, GGAS, GSOLV, and TOTAL represent van der Waals force, electrostatic energy, polar solvation energy, non-polar solvation energy, molecular mechanics term, solvation energy term, and total binding free energy, respectively).
[0068] In summary, based on the integrated pharmacology platform, this invention explored the mechanism of Eleutherococcus senticosus in treating non-small cell lung cancer through GEO sequencing data combined with network pharmacology, molecular docking, and molecular dynamics techniques, discovered and predicted its mechanism of action and related target genes in treating lung cancer. Eleutherococcus senticosus plays an important role in anti-non-small cell lung cancer by affecting potential targets such as IL6, MMP9, CCL2, TLR4, EDN1, and NFKBIA. GO functional enrichment analysis indicates that the chemical components of Eleutherococcus senticosus - non-small cell lung cancer potential targets are mainly involved in a series of biological processes such as prostaglandin receptor activity regulation, cell chemotaxis, histamine receptor activity, hormone levels, and serine-type endopeptidase activity; and KEGG pathway signal enrichment analysis indicates that the mechanism of action of Eleutherococcus senticosus on non-small cell lung cancer is closely related to signaling pathways such as Cellular senescence, IL-17 signaling pathway, Neuroactive ligand-receptor interaction signaling pathway, and p53 signaling pathway. This provides a theoretical basis for the clinical use of Eleutherococcus senticosus and research ideas and directions for subsequent studies.
Claims
1. A method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation, characterized in that It is done in the following way: S1: Screening of active ingredients in Acanthopanax senticosus According to the Chinese medicine system pharmacology database and the TCMSP database of the analysis platform, screening conditions were set, and the potential active ingredients of Acanthopanax senticosus were obtained and annotated according to OB and DL screening; the screening conditions were: OB>30%, DL>0.18; S2: Obtain potential targets for the treatment of non-small cell lung cancer with chemical components of Acanthopanax senticosus and disease targets for non-small cell lung cancer The chemical components of Acanthopanax senticosus were collected using the database Pub Chem in SMILE In ChI and SDF structure, and were screened in the GeneCards database using "non-small cell lung cancer" as the keyword and Score ≥ 5. The Venn diagram was drawn using DrawVenn Diagram to obtain the potential targets of Acanthopanax senticosus chemical components for the treatment of non-small cell lung cancer. The expression profile data of the dataset "TCGA-NSCLC, TCGA-NSCLC" were obtained from the UCSC Xena database, and the relevant clinical information was obtained from GDCTCGA. The R limma package was used to perform differential analysis on the TCGA data and select genes with significant differences as disease targets for non-small cell lung cancer. S3: Construction and analysis of the chemical components-target-disease network of Acanthopanax senticosus The active ingredients obtained by S1, the potential targets of Acanthopanax senticosus chemical components obtained by S2 for the treatment of non-small cell lung cancer, and the disease target information of non-small cell lung cancer were used to construct a network diagram of the relationship between the three using Cytoscape_; S4: Construction of protein interaction network The STRING database was used to analyze the network diagram of Acanthopanax senticosus chemical components-common targets of non-small cell lung cancer constructed by S3, and the Cytoscape software was used to draw the protein interaction network diagram, establish the protein interaction network, and obtain the protein interaction network relationship table; S5: KEGG pathway enrichment analysis and GO function enrichment analysis to explore the specific mechanism of anti-non-small cell lung cancer Metascape database was used to conduct KEGG pathway enrichment analysis and GO functional enrichment analysis on the potential targets of Acanthopanax senticosus chemical components in non-small cell lung cancer, and GOCC, GOMF, GOBP, and KEGG related data were obtained; S6: Construction and analysis of the chemical components-target-pathway-disease network of Acanthopanax senticosus The TOP pathways and their key targets and the active ingredients screened in S1 were selected to construct the "Acanthopanax senticosus chemical components-targets-pathways-diseases" network; S7: Molecular Docking The top two key targets in the S4 protein interaction network were selected for molecular docking with the main active ingredients, and the core targets and key ingredients of Acanthopanax senticosus were found to be closely related to lung cancer. The 3D structures of the main active chemical components of Acanthopanax senticosus were downloaded from the Pub Chem database, and the 3D structures of the core target proteins were downloaded from the Protein Data Bank database. S8: Molecular dynamics simulations to assess the binding stability of the protein-ligand complex.
2. The method according to claim 1, characterized in that the method is based on network pharmacology, molecular docking and molecular dynamics simulation to analyze the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer. The key components of Acanthopanax senticosus described in S7 are emodin, quercetin, and kaempferol, and the core targets are CCL2, IL6, and MMP9.
3. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that The chemical components of Acanthopanax senticosus described in S2 were collected using the database Pub Chem in SMILE InChI and SDF structures, and then imported into Swiss ADME for class indicator evaluation and Target Net / BATMAN-TCM / Swiss Target Prediction for target prediction. The acquired targets were summarized and deduplicated using the Uniprot specification.
4. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that After obtaining relevant clinical information from GDC TCGA as described in S2, the sample data with incomplete clinical information were excluded and a total of normal: 375 and Tumor: 1011 non-small cell lung cancer transcriptome data were obtained.
5. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that The disease targets of non-small cell lung cancer obtained by screening as described in S2 were screened for differentially expressed genes based on |logFC|>1.5 and P<0.
05. The pheatmap 1.0.12 package in the R language was used to draw the heat map, and the ggplot 23.4.2 package was used to draw the volcano map.
6. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that The protein interaction network established in S4 is as follows: The chemical components of Acanthopanax senticosus and common targets of non-small cell lung cancer obtained from the S3 network diagram were imported into the STRING database to establish a PPI network. The species selection was set to Homo sapiens, the confidence was set to ≥0.4, the free nodes were hidden, the TSV file was found and downloaded, and the PPI network diagram was drawn using cytospace to establish the PPI network diagram.
7. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that In S5, GO enrichment analysis used P < 0.05 as the screening condition, and the top 20 biological items with P values less than 0.05 in the results were selected to draw bar graphs; KEGG enrichment analysis also used P < 0.05 as the significant enrichment screening condition, and the top 30 signal pathways with P < 0.05 were selected to draw bar graphs and bubble graphs.
8. The method of claim 1 for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation, characterized in that The 3D structure of the core target protein in S7 was downloaded from the ProteinData Bank database. The core target protein was preliminarily processed using Pymol 2.6.0 software, and the protein and ligand molecules were pre-processed using AutoDock Tools 1.5.7 software.
9. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that The molecular docking between the key targets and the main active ingredients in S7 was completed by Autodock Vina. After the molecular docking was completed, the docking results were visualized using Py MOL software.
10. The method for analyzing the mechanism of action of Acanthopanax senticosus in treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation according to claim 1, characterized in that S8 molecular dynamics simulation was used to evaluate the binding stability of the protein-ligand complex as follows: Gromacs v2023.4 software was used to perform a 100 ns molecular dynamics simulation of the complex obtained by molecular docking, the pdb2gmx module was used to construct the protein topology, and the force field was Amber14sb; the complex format was converted, the force field was added, the solvation and charge were adjusted, and after energy minimization, the system was heated and balanced, a 100 ns simulation was performed, various parameters were calculated, the free energy was analyzed using Gromacs, and the data was extracted to calculate the binding free energy and visualize it.